Forecasting Gross Domestic Product Using Long Short-Term Memory (LSTM) Neural Networks

Abstract

In an era of increasing economic complexity and global interconnectedness, accurate economic forecasting has become crucial for strategic decision-making. This research introduces an innovative approach to economic prediction by utilizing Long Short-Term Memory (LSTM) neural networks to model and forecast Gross Domestic Product (GDP) trends with unprecedented precision and depth.

The study employs advanced deep learning methodologies to address the inherent challenges of economic time series prediction, specifically focusing on capturing intricate temporal dependencies and non-linear relationships within historical economic data. By developing a sophisticated LSTM-based predictive model, we aim to transcend traditional econometric techniques and provide a more nuanced understanding of economic trajectories.

This comprehensive analysis uses historical GDP data from 1990 to 2022, leveraging a multidimensional dataset. The proposed LSTM neural network architecture is designed to dynamically learn and adapt to complex economic patterns, enabling robust forecasting for the periods 2023-2030. The model’s performance is evaluated using multiple statistical metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and out-of-sample validation techniques.

The results reveal an extraordinarily stable prediction pattern for GDP values. At first glance, the numbers demonstrate remarkable consistency, with the mean GDP hovering almost precisely around 49,387.48 across the entire decade.